Statistical personalization of ventricular fiber orientation using shape predictors

Karim Lekadir, Corne Hoogendoorn, Marco Pereanez, Xenia Alba, Ali Pashaei, Alejandro F. Frangi

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a predictive framework for the statistical personalization of ventricular fibers. To this end, the relationship between subject-specific geometry of the left (LV) and right ventricles (RV) and fiber orientation is learned statistically from a training sample of ex vivo diffusion tensor imaging datasets. More specifically, the axes in the shape space which correlate most with the myocardial fiber orientations are extracted and used for prediction in new subjects. With this approach and unlike existing fiber models, inter-subject variability is taken into account to generate latent shape predictors that are statistically optimal to estimate fiber orientation at each individual myocardial location. The proposed predictive model was applied to the task of personalizing fibers in 10 canine subjects. The results indicate that the ventricular shapes are good predictors of fiber orientation, with an improvement of 11.4% in accuracy over the average fiber model.

Original languageEnglish
Article number6701201
Pages (from-to)882-890
Number of pages9
JournalIEEE transactions on medical imaging
Volume33
Issue number4
DOIs
Publication statusPublished - Apr 2014

Keywords

  • Cardiac fiber structure
  • cardiac simulation
  • diffusion tensor imaging
  • partial least squares regression
  • Predictive modeling

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